Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Sunday 26 July 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 186,035 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 186,025 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 14
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 7
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 1
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 8
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 4
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 5
## 116 2020-06-24 East of England 4
## 117 2020-06-25 East of England 1
## 118 2020-06-26 East of England 5
## 119 2020-06-27 East of England 6
## 120 2020-06-28 East of England 8
## 121 2020-06-29 East of England 4
## 122 2020-06-30 East of England 5
## 123 2020-07-01 East of England 2
## 124 2020-07-02 East of England 5
## 125 2020-07-03 East of England 0
## 126 2020-07-04 East of England 3
## 127 2020-07-05 East of England 1
## 128 2020-07-06 East of England 2
## 129 2020-07-07 East of England 2
## 130 2020-07-08 East of England 0
## 131 2020-07-09 East of England 8
## 132 2020-07-10 East of England 4
## 133 2020-07-11 East of England 2
## 134 2020-07-12 East of England 1
## 135 2020-07-13 East of England 7
## 136 2020-07-14 East of England 2
## 137 2020-07-15 East of England 0
## 138 2020-07-16 East of England 0
## 139 2020-07-17 East of England 0
## 140 2020-07-18 East of England 0
## 141 2020-07-19 East of England 1
## 142 2020-07-20 East of England 1
## 143 2020-07-21 East of England 0
## 144 2020-07-22 East of England 0
## 145 2020-07-23 East of England 0
## 146 2020-07-24 East of England 0
## 147 2020-03-01 London 0
## 148 2020-03-02 London 0
## 149 2020-03-03 London 0
## 150 2020-03-04 London 0
## 151 2020-03-05 London 0
## 152 2020-03-06 London 1
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## 294 2020-03-02 Midlands 0
## 295 2020-03-03 Midlands 1
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## 300 2020-03-08 Midlands 2
## 301 2020-03-09 Midlands 1
## 302 2020-03-10 Midlands 0
## 303 2020-03-11 Midlands 2
## 304 2020-03-12 Midlands 6
## 305 2020-03-13 Midlands 5
## 306 2020-03-14 Midlands 4
## 307 2020-03-15 Midlands 5
## 308 2020-03-16 Midlands 11
## 309 2020-03-17 Midlands 8
## 310 2020-03-18 Midlands 13
## 311 2020-03-19 Midlands 8
## 312 2020-03-20 Midlands 28
## 313 2020-03-21 Midlands 13
## 314 2020-03-22 Midlands 31
## 315 2020-03-23 Midlands 33
## 316 2020-03-24 Midlands 41
## 317 2020-03-25 Midlands 48
## 318 2020-03-26 Midlands 64
## 319 2020-03-27 Midlands 72
## 320 2020-03-28 Midlands 89
## 321 2020-03-29 Midlands 92
## 322 2020-03-30 Midlands 90
## 323 2020-03-31 Midlands 123
## 324 2020-04-01 Midlands 140
## 325 2020-04-02 Midlands 142
## 326 2020-04-03 Midlands 124
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## 330 2020-04-07 Midlands 123
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## 342 2020-04-19 Midlands 92
## 343 2020-04-20 Midlands 107
## 344 2020-04-21 Midlands 86
## 345 2020-04-22 Midlands 78
## 346 2020-04-23 Midlands 103
## 347 2020-04-24 Midlands 79
## 348 2020-04-25 Midlands 72
## 349 2020-04-26 Midlands 81
## 350 2020-04-27 Midlands 74
## 351 2020-04-28 Midlands 68
## 352 2020-04-29 Midlands 53
## 353 2020-04-30 Midlands 56
## 354 2020-05-01 Midlands 64
## 355 2020-05-02 Midlands 51
## 356 2020-05-03 Midlands 52
## 357 2020-05-04 Midlands 61
## 358 2020-05-05 Midlands 59
## 359 2020-05-06 Midlands 59
## 360 2020-05-07 Midlands 48
## 361 2020-05-08 Midlands 34
## 362 2020-05-09 Midlands 37
## 363 2020-05-10 Midlands 42
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## 365 2020-05-12 Midlands 45
## 366 2020-05-13 Midlands 40
## 367 2020-05-14 Midlands 38
## 368 2020-05-15 Midlands 40
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## 370 2020-05-17 Midlands 31
## 371 2020-05-18 Midlands 36
## 372 2020-05-19 Midlands 35
## 373 2020-05-20 Midlands 36
## 374 2020-05-21 Midlands 32
## 375 2020-05-22 Midlands 27
## 376 2020-05-23 Midlands 34
## 377 2020-05-24 Midlands 20
## 378 2020-05-25 Midlands 26
## 379 2020-05-26 Midlands 33
## 380 2020-05-27 Midlands 29
## 381 2020-05-28 Midlands 28
## 382 2020-05-29 Midlands 20
## 383 2020-05-30 Midlands 21
## 384 2020-05-31 Midlands 22
## 385 2020-06-01 Midlands 20
## 386 2020-06-02 Midlands 22
## 387 2020-06-03 Midlands 24
## 388 2020-06-04 Midlands 16
## 389 2020-06-05 Midlands 21
## 390 2020-06-06 Midlands 20
## 391 2020-06-07 Midlands 17
## 392 2020-06-08 Midlands 16
## 393 2020-06-09 Midlands 18
## 394 2020-06-10 Midlands 15
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## 417 2020-07-03 Midlands 3
## 418 2020-07-04 Midlands 4
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## 427 2020-07-13 Midlands 1
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## 430 2020-07-16 Midlands 2
## 431 2020-07-17 Midlands 2
## 432 2020-07-18 Midlands 3
## 433 2020-07-19 Midlands 3
## 434 2020-07-20 Midlands 3
## 435 2020-07-21 Midlands 0
## 436 2020-07-22 Midlands 2
## 437 2020-07-23 Midlands 5
## 438 2020-07-24 Midlands 0
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## 440 2020-03-02 North East and Yorkshire 0
## 441 2020-03-03 North East and Yorkshire 0
## 442 2020-03-04 North East and Yorkshire 0
## 443 2020-03-05 North East and Yorkshire 0
## 444 2020-03-06 North East and Yorkshire 0
## 445 2020-03-07 North East and Yorkshire 0
## 446 2020-03-08 North East and Yorkshire 0
## 447 2020-03-09 North East and Yorkshire 0
## 448 2020-03-10 North East and Yorkshire 0
## 449 2020-03-11 North East and Yorkshire 0
## 450 2020-03-12 North East and Yorkshire 0
## 451 2020-03-13 North East and Yorkshire 0
## 452 2020-03-14 North East and Yorkshire 0
## 453 2020-03-15 North East and Yorkshire 2
## 454 2020-03-16 North East and Yorkshire 3
## 455 2020-03-17 North East and Yorkshire 1
## 456 2020-03-18 North East and Yorkshire 2
## 457 2020-03-19 North East and Yorkshire 6
## 458 2020-03-20 North East and Yorkshire 5
## 459 2020-03-21 North East and Yorkshire 6
## 460 2020-03-22 North East and Yorkshire 7
## 461 2020-03-23 North East and Yorkshire 9
## 462 2020-03-24 North East and Yorkshire 8
## 463 2020-03-25 North East and Yorkshire 18
## 464 2020-03-26 North East and Yorkshire 21
## 465 2020-03-27 North East and Yorkshire 28
## 466 2020-03-28 North East and Yorkshire 35
## 467 2020-03-29 North East and Yorkshire 38
## 468 2020-03-30 North East and Yorkshire 64
## 469 2020-03-31 North East and Yorkshire 60
## 470 2020-04-01 North East and Yorkshire 67
## 471 2020-04-02 North East and Yorkshire 75
## 472 2020-04-03 North East and Yorkshire 100
## 473 2020-04-04 North East and Yorkshire 105
## 474 2020-04-05 North East and Yorkshire 92
## 475 2020-04-06 North East and Yorkshire 96
## 476 2020-04-07 North East and Yorkshire 102
## 477 2020-04-08 North East and Yorkshire 107
## 478 2020-04-09 North East and Yorkshire 111
## 479 2020-04-10 North East and Yorkshire 117
## 480 2020-04-11 North East and Yorkshire 98
## 481 2020-04-12 North East and Yorkshire 84
## 482 2020-04-13 North East and Yorkshire 94
## 483 2020-04-14 North East and Yorkshire 107
## 484 2020-04-15 North East and Yorkshire 96
## 485 2020-04-16 North East and Yorkshire 103
## 486 2020-04-17 North East and Yorkshire 88
## 487 2020-04-18 North East and Yorkshire 95
## 488 2020-04-19 North East and Yorkshire 88
## 489 2020-04-20 North East and Yorkshire 100
## 490 2020-04-21 North East and Yorkshire 76
## 491 2020-04-22 North East and Yorkshire 84
## 492 2020-04-23 North East and Yorkshire 63
## 493 2020-04-24 North East and Yorkshire 72
## 494 2020-04-25 North East and Yorkshire 69
## 495 2020-04-26 North East and Yorkshire 65
## 496 2020-04-27 North East and Yorkshire 65
## 497 2020-04-28 North East and Yorkshire 57
## 498 2020-04-29 North East and Yorkshire 69
## 499 2020-04-30 North East and Yorkshire 57
## 500 2020-05-01 North East and Yorkshire 64
## 501 2020-05-02 North East and Yorkshire 48
## 502 2020-05-03 North East and Yorkshire 40
## 503 2020-05-04 North East and Yorkshire 49
## 504 2020-05-05 North East and Yorkshire 40
## 505 2020-05-06 North East and Yorkshire 51
## 506 2020-05-07 North East and Yorkshire 45
## 507 2020-05-08 North East and Yorkshire 42
## 508 2020-05-09 North East and Yorkshire 44
## 509 2020-05-10 North East and Yorkshire 40
## 510 2020-05-11 North East and Yorkshire 29
## 511 2020-05-12 North East and Yorkshire 27
## 512 2020-05-13 North East and Yorkshire 28
## 513 2020-05-14 North East and Yorkshire 31
## 514 2020-05-15 North East and Yorkshire 32
## 515 2020-05-16 North East and Yorkshire 35
## 516 2020-05-17 North East and Yorkshire 26
## 517 2020-05-18 North East and Yorkshire 30
## 518 2020-05-19 North East and Yorkshire 27
## 519 2020-05-20 North East and Yorkshire 22
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## 521 2020-05-22 North East and Yorkshire 22
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## 523 2020-05-24 North East and Yorkshire 26
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## 528 2020-05-29 North East and Yorkshire 25
## 529 2020-05-30 North East and Yorkshire 20
## 530 2020-05-31 North East and Yorkshire 20
## 531 2020-06-01 North East and Yorkshire 17
## 532 2020-06-02 North East and Yorkshire 23
## 533 2020-06-03 North East and Yorkshire 23
## 534 2020-06-04 North East and Yorkshire 17
## 535 2020-06-05 North East and Yorkshire 18
## 536 2020-06-06 North East and Yorkshire 21
## 537 2020-06-07 North East and Yorkshire 14
## 538 2020-06-08 North East and Yorkshire 11
## 539 2020-06-09 North East and Yorkshire 12
## 540 2020-06-10 North East and Yorkshire 19
## 541 2020-06-11 North East and Yorkshire 7
## 542 2020-06-12 North East and Yorkshire 9
## 543 2020-06-13 North East and Yorkshire 10
## 544 2020-06-14 North East and Yorkshire 11
## 545 2020-06-15 North East and Yorkshire 9
## 546 2020-06-16 North East and Yorkshire 10
## 547 2020-06-17 North East and Yorkshire 9
## 548 2020-06-18 North East and Yorkshire 11
## 549 2020-06-19 North East and Yorkshire 6
## 550 2020-06-20 North East and Yorkshire 5
## 551 2020-06-21 North East and Yorkshire 4
## 552 2020-06-22 North East and Yorkshire 7
## 553 2020-06-23 North East and Yorkshire 7
## 554 2020-06-24 North East and Yorkshire 10
## 555 2020-06-25 North East and Yorkshire 4
## 556 2020-06-26 North East and Yorkshire 7
## 557 2020-06-27 North East and Yorkshire 3
## 558 2020-06-28 North East and Yorkshire 5
## 559 2020-06-29 North East and Yorkshire 2
## 560 2020-06-30 North East and Yorkshire 6
## 561 2020-07-01 North East and Yorkshire 1
## 562 2020-07-02 North East and Yorkshire 4
## 563 2020-07-03 North East and Yorkshire 4
## 564 2020-07-04 North East and Yorkshire 4
## 565 2020-07-05 North East and Yorkshire 2
## 566 2020-07-06 North East and Yorkshire 2
## 567 2020-07-07 North East and Yorkshire 3
## 568 2020-07-08 North East and Yorkshire 3
## 569 2020-07-09 North East and Yorkshire 0
## 570 2020-07-10 North East and Yorkshire 3
## 571 2020-07-11 North East and Yorkshire 1
## 572 2020-07-12 North East and Yorkshire 4
## 573 2020-07-13 North East and Yorkshire 1
## 574 2020-07-14 North East and Yorkshire 1
## 575 2020-07-15 North East and Yorkshire 2
## 576 2020-07-16 North East and Yorkshire 2
## 577 2020-07-17 North East and Yorkshire 1
## 578 2020-07-18 North East and Yorkshire 2
## 579 2020-07-19 North East and Yorkshire 2
## 580 2020-07-20 North East and Yorkshire 1
## 581 2020-07-21 North East and Yorkshire 1
## 582 2020-07-22 North East and Yorkshire 4
## 583 2020-07-23 North East and Yorkshire 0
## 584 2020-07-24 North East and Yorkshire 0
## 585 2020-03-01 North West 0
## 586 2020-03-02 North West 0
## 587 2020-03-03 North West 0
## 588 2020-03-04 North West 0
## 589 2020-03-05 North West 1
## 590 2020-03-06 North West 0
## 591 2020-03-07 North West 0
## 592 2020-03-08 North West 1
## 593 2020-03-09 North West 0
## 594 2020-03-10 North West 0
## 595 2020-03-11 North West 0
## 596 2020-03-12 North West 2
## 597 2020-03-13 North West 3
## 598 2020-03-14 North West 1
## 599 2020-03-15 North West 4
## 600 2020-03-16 North West 2
## 601 2020-03-17 North West 4
## 602 2020-03-18 North West 6
## 603 2020-03-19 North West 7
## 604 2020-03-20 North West 10
## 605 2020-03-21 North West 11
## 606 2020-03-22 North West 13
## 607 2020-03-23 North West 15
## 608 2020-03-24 North West 21
## 609 2020-03-25 North West 21
## 610 2020-03-26 North West 29
## 611 2020-03-27 North West 36
## 612 2020-03-28 North West 28
## 613 2020-03-29 North West 46
## 614 2020-03-30 North West 67
## 615 2020-03-31 North West 52
## 616 2020-04-01 North West 86
## 617 2020-04-02 North West 96
## 618 2020-04-03 North West 95
## 619 2020-04-04 North West 98
## 620 2020-04-05 North West 102
## 621 2020-04-06 North West 100
## 622 2020-04-07 North West 135
## 623 2020-04-08 North West 127
## 624 2020-04-09 North West 119
## 625 2020-04-10 North West 117
## 626 2020-04-11 North West 138
## 627 2020-04-12 North West 125
## 628 2020-04-13 North West 129
## 629 2020-04-14 North West 131
## 630 2020-04-15 North West 114
## 631 2020-04-16 North West 135
## 632 2020-04-17 North West 98
## 633 2020-04-18 North West 113
## 634 2020-04-19 North West 71
## 635 2020-04-20 North West 83
## 636 2020-04-21 North West 76
## 637 2020-04-22 North West 86
## 638 2020-04-23 North West 85
## 639 2020-04-24 North West 66
## 640 2020-04-25 North West 66
## 641 2020-04-26 North West 55
## 642 2020-04-27 North West 54
## 643 2020-04-28 North West 57
## 644 2020-04-29 North West 63
## 645 2020-04-30 North West 59
## 646 2020-05-01 North West 45
## 647 2020-05-02 North West 56
## 648 2020-05-03 North West 55
## 649 2020-05-04 North West 48
## 650 2020-05-05 North West 48
## 651 2020-05-06 North West 44
## 652 2020-05-07 North West 49
## 653 2020-05-08 North West 42
## 654 2020-05-09 North West 31
## 655 2020-05-10 North West 42
## 656 2020-05-11 North West 35
## 657 2020-05-12 North West 38
## 658 2020-05-13 North West 25
## 659 2020-05-14 North West 26
## 660 2020-05-15 North West 33
## 661 2020-05-16 North West 32
## 662 2020-05-17 North West 24
## 663 2020-05-18 North West 31
## 664 2020-05-19 North West 35
## 665 2020-05-20 North West 27
## 666 2020-05-21 North West 27
## 667 2020-05-22 North West 26
## 668 2020-05-23 North West 31
## 669 2020-05-24 North West 26
## 670 2020-05-25 North West 31
## 671 2020-05-26 North West 27
## 672 2020-05-27 North West 27
## 673 2020-05-28 North West 28
## 674 2020-05-29 North West 20
## 675 2020-05-30 North West 19
## 676 2020-05-31 North West 13
## 677 2020-06-01 North West 12
## 678 2020-06-02 North West 27
## 679 2020-06-03 North West 22
## 680 2020-06-04 North West 22
## 681 2020-06-05 North West 16
## 682 2020-06-06 North West 26
## 683 2020-06-07 North West 20
## 684 2020-06-08 North West 23
## 685 2020-06-09 North West 17
## 686 2020-06-10 North West 16
## 687 2020-06-11 North West 16
## 688 2020-06-12 North West 11
## 689 2020-06-13 North West 10
## 690 2020-06-14 North West 15
## 691 2020-06-15 North West 16
## 692 2020-06-16 North West 15
## 693 2020-06-17 North West 13
## 694 2020-06-18 North West 14
## 695 2020-06-19 North West 7
## 696 2020-06-20 North West 11
## 697 2020-06-21 North West 8
## 698 2020-06-22 North West 11
## 699 2020-06-23 North West 13
## 700 2020-06-24 North West 13
## 701 2020-06-25 North West 15
## 702 2020-06-26 North West 6
## 703 2020-06-27 North West 7
## 704 2020-06-28 North West 9
## 705 2020-06-29 North West 9
## 706 2020-06-30 North West 7
## 707 2020-07-01 North West 3
## 708 2020-07-02 North West 6
## 709 2020-07-03 North West 7
## 710 2020-07-04 North West 4
## 711 2020-07-05 North West 6
## 712 2020-07-06 North West 9
## 713 2020-07-07 North West 8
## 714 2020-07-08 North West 5
## 715 2020-07-09 North West 10
## 716 2020-07-10 North West 2
## 717 2020-07-11 North West 4
## 718 2020-07-12 North West 0
## 719 2020-07-13 North West 6
## 720 2020-07-14 North West 4
## 721 2020-07-15 North West 5
## 722 2020-07-16 North West 2
## 723 2020-07-17 North West 4
## 724 2020-07-18 North West 4
## 725 2020-07-19 North West 2
## 726 2020-07-20 North West 0
## 727 2020-07-21 North West 1
## 728 2020-07-22 North West 0
## 729 2020-07-23 North West 1
## 730 2020-07-24 North West 0
## 731 2020-03-01 South East 0
## 732 2020-03-02 South East 0
## 733 2020-03-03 South East 1
## 734 2020-03-04 South East 0
## 735 2020-03-05 South East 1
## 736 2020-03-06 South East 0
## 737 2020-03-07 South East 0
## 738 2020-03-08 South East 1
## 739 2020-03-09 South East 1
## 740 2020-03-10 South East 1
## 741 2020-03-11 South East 1
## 742 2020-03-12 South East 0
## 743 2020-03-13 South East 1
## 744 2020-03-14 South East 1
## 745 2020-03-15 South East 5
## 746 2020-03-16 South East 8
## 747 2020-03-17 South East 7
## 748 2020-03-18 South East 10
## 749 2020-03-19 South East 9
## 750 2020-03-20 South East 13
## 751 2020-03-21 South East 7
## 752 2020-03-22 South East 25
## 753 2020-03-23 South East 20
## 754 2020-03-24 South East 22
## 755 2020-03-25 South East 29
## 756 2020-03-26 South East 35
## 757 2020-03-27 South East 34
## 758 2020-03-28 South East 36
## 759 2020-03-29 South East 55
## 760 2020-03-30 South East 58
## 761 2020-03-31 South East 65
## 762 2020-04-01 South East 66
## 763 2020-04-02 South East 55
## 764 2020-04-03 South East 72
## 765 2020-04-04 South East 80
## 766 2020-04-05 South East 82
## 767 2020-04-06 South East 88
## 768 2020-04-07 South East 100
## 769 2020-04-08 South East 83
## 770 2020-04-09 South East 104
## 771 2020-04-10 South East 88
## 772 2020-04-11 South East 88
## 773 2020-04-12 South East 88
## 774 2020-04-13 South East 84
## 775 2020-04-14 South East 65
## 776 2020-04-15 South East 72
## 777 2020-04-16 South East 56
## 778 2020-04-17 South East 86
## 779 2020-04-18 South East 57
## 780 2020-04-19 South East 70
## 781 2020-04-20 South East 87
## 782 2020-04-21 South East 51
## 783 2020-04-22 South East 54
## 784 2020-04-23 South East 57
## 785 2020-04-24 South East 64
## 786 2020-04-25 South East 51
## 787 2020-04-26 South East 51
## 788 2020-04-27 South East 41
## 789 2020-04-28 South East 40
## 790 2020-04-29 South East 47
## 791 2020-04-30 South East 29
## 792 2020-05-01 South East 37
## 793 2020-05-02 South East 36
## 794 2020-05-03 South East 17
## 795 2020-05-04 South East 35
## 796 2020-05-05 South East 29
## 797 2020-05-06 South East 25
## 798 2020-05-07 South East 27
## 799 2020-05-08 South East 26
## 800 2020-05-09 South East 28
## 801 2020-05-10 South East 19
## 802 2020-05-11 South East 25
## 803 2020-05-12 South East 27
## 804 2020-05-13 South East 18
## 805 2020-05-14 South East 32
## 806 2020-05-15 South East 25
## 807 2020-05-16 South East 22
## 808 2020-05-17 South East 18
## 809 2020-05-18 South East 22
## 810 2020-05-19 South East 12
## 811 2020-05-20 South East 22
## 812 2020-05-21 South East 15
## 813 2020-05-22 South East 17
## 814 2020-05-23 South East 21
## 815 2020-05-24 South East 17
## 816 2020-05-25 South East 13
## 817 2020-05-26 South East 19
## 818 2020-05-27 South East 18
## 819 2020-05-28 South East 12
## 820 2020-05-29 South East 21
## 821 2020-05-30 South East 8
## 822 2020-05-31 South East 12
## 823 2020-06-01 South East 11
## 824 2020-06-02 South East 13
## 825 2020-06-03 South East 18
## 826 2020-06-04 South East 11
## 827 2020-06-05 South East 11
## 828 2020-06-06 South East 10
## 829 2020-06-07 South East 12
## 830 2020-06-08 South East 8
## 831 2020-06-09 South East 10
## 832 2020-06-10 South East 11
## 833 2020-06-11 South East 5
## 834 2020-06-12 South East 6
## 835 2020-06-13 South East 7
## 836 2020-06-14 South East 7
## 837 2020-06-15 South East 8
## 838 2020-06-16 South East 13
## 839 2020-06-17 South East 9
## 840 2020-06-18 South East 4
## 841 2020-06-19 South East 7
## 842 2020-06-20 South East 5
## 843 2020-06-21 South East 3
## 844 2020-06-22 South East 2
## 845 2020-06-23 South East 8
## 846 2020-06-24 South East 7
## 847 2020-06-25 South East 5
## 848 2020-06-26 South East 8
## 849 2020-06-27 South East 8
## 850 2020-06-28 South East 6
## 851 2020-06-29 South East 5
## 852 2020-06-30 South East 5
## 853 2020-07-01 South East 2
## 854 2020-07-02 South East 7
## 855 2020-07-03 South East 3
## 856 2020-07-04 South East 6
## 857 2020-07-05 South East 4
## 858 2020-07-06 South East 4
## 859 2020-07-07 South East 6
## 860 2020-07-08 South East 3
## 861 2020-07-09 South East 7
## 862 2020-07-10 South East 3
## 863 2020-07-11 South East 3
## 864 2020-07-12 South East 4
## 865 2020-07-13 South East 4
## 866 2020-07-14 South East 5
## 867 2020-07-15 South East 6
## 868 2020-07-16 South East 3
## 869 2020-07-17 South East 1
## 870 2020-07-18 South East 4
## 871 2020-07-19 South East 2
## 872 2020-07-20 South East 3
## 873 2020-07-21 South East 4
## 874 2020-07-22 South East 2
## 875 2020-07-23 South East 2
## 876 2020-07-24 South East 0
## 877 2020-03-01 South West 0
## 878 2020-03-02 South West 0
## 879 2020-03-03 South West 0
## 880 2020-03-04 South West 0
## 881 2020-03-05 South West 0
## 882 2020-03-06 South West 0
## 883 2020-03-07 South West 0
## 884 2020-03-08 South West 0
## 885 2020-03-09 South West 0
## 886 2020-03-10 South West 0
## 887 2020-03-11 South West 1
## 888 2020-03-12 South West 0
## 889 2020-03-13 South West 0
## 890 2020-03-14 South West 1
## 891 2020-03-15 South West 0
## 892 2020-03-16 South West 0
## 893 2020-03-17 South West 2
## 894 2020-03-18 South West 2
## 895 2020-03-19 South West 4
## 896 2020-03-20 South West 3
## 897 2020-03-21 South West 6
## 898 2020-03-22 South West 7
## 899 2020-03-23 South West 8
## 900 2020-03-24 South West 7
## 901 2020-03-25 South West 9
## 902 2020-03-26 South West 11
## 903 2020-03-27 South West 13
## 904 2020-03-28 South West 21
## 905 2020-03-29 South West 18
## 906 2020-03-30 South West 23
## 907 2020-03-31 South West 23
## 908 2020-04-01 South West 21
## 909 2020-04-02 South West 23
## 910 2020-04-03 South West 30
## 911 2020-04-04 South West 42
## 912 2020-04-05 South West 32
## 913 2020-04-06 South West 34
## 914 2020-04-07 South West 39
## 915 2020-04-08 South West 47
## 916 2020-04-09 South West 24
## 917 2020-04-10 South West 46
## 918 2020-04-11 South West 43
## 919 2020-04-12 South West 23
## 920 2020-04-13 South West 27
## 921 2020-04-14 South West 24
## 922 2020-04-15 South West 32
## 923 2020-04-16 South West 29
## 924 2020-04-17 South West 33
## 925 2020-04-18 South West 25
## 926 2020-04-19 South West 31
## 927 2020-04-20 South West 26
## 928 2020-04-21 South West 26
## 929 2020-04-22 South West 23
## 930 2020-04-23 South West 17
## 931 2020-04-24 South West 19
## 932 2020-04-25 South West 15
## 933 2020-04-26 South West 27
## 934 2020-04-27 South West 13
## 935 2020-04-28 South West 17
## 936 2020-04-29 South West 15
## 937 2020-04-30 South West 26
## 938 2020-05-01 South West 6
## 939 2020-05-02 South West 7
## 940 2020-05-03 South West 10
## 941 2020-05-04 South West 17
## 942 2020-05-05 South West 14
## 943 2020-05-06 South West 19
## 944 2020-05-07 South West 16
## 945 2020-05-08 South West 6
## 946 2020-05-09 South West 11
## 947 2020-05-10 South West 5
## 948 2020-05-11 South West 8
## 949 2020-05-12 South West 7
## 950 2020-05-13 South West 7
## 951 2020-05-14 South West 6
## 952 2020-05-15 South West 4
## 953 2020-05-16 South West 4
## 954 2020-05-17 South West 6
## 955 2020-05-18 South West 4
## 956 2020-05-19 South West 6
## 957 2020-05-20 South West 1
## 958 2020-05-21 South West 9
## 959 2020-05-22 South West 6
## 960 2020-05-23 South West 6
## 961 2020-05-24 South West 3
## 962 2020-05-25 South West 8
## 963 2020-05-26 South West 11
## 964 2020-05-27 South West 5
## 965 2020-05-28 South West 10
## 966 2020-05-29 South West 7
## 967 2020-05-30 South West 3
## 968 2020-05-31 South West 2
## 969 2020-06-01 South West 7
## 970 2020-06-02 South West 2
## 971 2020-06-03 South West 7
## 972 2020-06-04 South West 2
## 973 2020-06-05 South West 2
## 974 2020-06-06 South West 1
## 975 2020-06-07 South West 3
## 976 2020-06-08 South West 3
## 977 2020-06-09 South West 0
## 978 2020-06-10 South West 1
## 979 2020-06-11 South West 2
## 980 2020-06-12 South West 2
## 981 2020-06-13 South West 2
## 982 2020-06-14 South West 0
## 983 2020-06-15 South West 2
## 984 2020-06-16 South West 2
## 985 2020-06-17 South West 0
## 986 2020-06-18 South West 0
## 987 2020-06-19 South West 0
## 988 2020-06-20 South West 2
## 989 2020-06-21 South West 0
## 990 2020-06-22 South West 1
## 991 2020-06-23 South West 1
## 992 2020-06-24 South West 1
## 993 2020-06-25 South West 0
## 994 2020-06-26 South West 3
## 995 2020-06-27 South West 0
## 996 2020-06-28 South West 0
## 997 2020-06-29 South West 1
## 998 2020-06-30 South West 0
## 999 2020-07-01 South West 0
## 1000 2020-07-02 South West 0
## 1001 2020-07-03 South West 0
## 1002 2020-07-04 South West 0
## 1003 2020-07-05 South West 1
## 1004 2020-07-06 South West 0
## 1005 2020-07-07 South West 0
## 1006 2020-07-08 South West 2
## 1007 2020-07-09 South West 0
## 1008 2020-07-10 South West 1
## 1009 2020-07-11 South West 0
## 1010 2020-07-12 South West 0
## 1011 2020-07-13 South West 1
## 1012 2020-07-14 South West 0
## 1013 2020-07-15 South West 0
## 1014 2020-07-16 South West 0
## 1015 2020-07-17 South West 1
## 1016 2020-07-18 South West 0
## 1017 2020-07-19 South West 0
## 1018 2020-07-20 South West 0
## 1019 2020-07-21 South West 0
## 1020 2020-07-22 South West 0
## 1021 2020-07-23 South West 0
## 1022 2020-07-24 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Thursday 23 Jul 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -14.3903 -4.9377 -0.5171 3.8610 9.1864
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.497e+00 6.868e-02 65.48 <2e-16 ***
## note_lag 1.535e-05 7.169e-07 21.41 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 28.70717)
##
## Null deviance: 14436.2 on 84 degrees of freedom
## Residual deviance: 2555.3 on 83 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 89.721252 1.000015
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 78.240058 102.419038
## note_lag 1.000014 1.000017
Rsq(lag_mod)
## [1] 0.822996
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.4.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.14
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.2.0
## [10] projections_0.5.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.2 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.6 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-148 fs_1.4.2 webshot_0.5.2 httr_1.4.2
## [5] rprojroot_1.3-2 tools_4.0.2 backports_1.1.8 utf8_1.1.4
## [9] R6_2.4.1 mgcv_1.8-31 DBI_1.1.0 colorspace_1.4-1
## [13] withr_2.2.0 gridExtra_2.3 tidyselect_1.1.0 sodium_1.1
## [17] curl_4.3 compiler_4.0.2 cli_2.0.2 labeling_0.3
## [21] matchmaker_0.1.1 scales_1.1.1 digest_0.6.25 foreign_0.8-80
## [25] rmarkdown_2.3 pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
## [29] htmlwidgets_1.5.1 rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11
## [33] farver_2.0.3 generics_0.0.2 jsonlite_1.7.0 crosstalk_1.1.0.1
## [37] car_3.0-8 zip_2.0.4 magrittr_1.5 kyotil_2019.11-22
## [41] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
## [45] viridis_0.5.1 abind_1.4-5 lifecycle_0.2.0 stringi_1.4.6
## [49] yaml_2.2.1 carData_3.0-4 snakecase_0.11.0 MASS_7.3-51.6
## [53] plyr_1.8.6 grid_4.0.2 blob_1.2.1 crayon_1.3.4
## [57] lattice_0.20-41 cowplot_1.0.0 splines_4.0.2 haven_2.3.1
## [61] hms_0.5.3 knitr_1.29 pillar_1.4.6 boot_1.3-25
## [65] ggsignif_0.6.0 reprex_0.3.0 glue_1.4.1 evaluate_0.14
## [69] data.table_1.13.0 modelr_0.1.8 vctrs_0.3.2 selectr_0.4-2
## [73] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1 xfun_0.16
## [77] openxlsx_4.1.5 broom_0.7.0 rstatix_0.6.0 survival_3.1-12
## [81] viridisLite_0.3.0 ellipsis_0.3.1